Simplification for cross-component linear model prediction mode

ABSTRACT

A method and apparatus for encoding or decoding a video sequence includes applying a Cross-Component Linear Model (CCLM) to a video sequence, and applying an interpolation filter in the Cross-Component Linear Model (CCLM), wherein the interpolation filter is dependent upon a YUV format of the video sequence.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.16/523,258 filed on Jul. 26, 2019, which claims priority under 35 U.S.C.§ 119 from U.S. Provisional Application No. 62/781,316 filed on Dec. 18,2018, U.S. Provisional Application No. 62/785,678 filed on Dec. 27,2018, U.S. Provisional Application No. 62/788,729 filed on Jan. 4, 2019,and U.S. Provisional Application No. 62/789,992 filed on Jan. 8, 2019,in the U.S. Patent & Trademark Office, the disclosures of which areincorporated herein by reference in their entireties.

FIELD

Methods and apparatuses consistent with embodiments relate to videoprocessing, and more particularly, encoding or decoding a video sequencewith a focus on simplifying Cross Component Linear Model predictionmodes.

BACKGROUND

Recently, the Video Coding Experts Group (VCEG) of the ITUTelecommunication Standardization Sector (ITU-T), a sector of theInternational Telecommunication Union (ITU), and the ISO/IEC MPEG (JTC1/SC 29/WG 11), a standardization subcommittee of the Joint TechnicalCommittee ISO/IEC JTC 1 of the International Organization forStandardization (ISO) and the International Electrotechnical Commission(IEC), published the H.265/High Efficiency Video Coding (HEVC) standardin 2013 (version 1). This standard was updated in 2014 to version 2, in2015 to version 3, and in 2016 to version 4.

In October 2017, they issued the Joint Call for Proposals on VideoCompression with Capability beyond HEVC (CfP). By Feb. 15, 2018, 22 CfPresponses on standard dynamic range (SDR), 12 CfP responses on highdynamic range (HDR), and 12 CfP responses on 360 video categories weresubmitted, respectively. In April 2018, all received CfP responses wereevaluated in the 122 MPEG/10th JVET meeting. As a result of thismeeting, JVET formally launched the standardization process ofnext-generation video coding beyond HEVC. The new standard was namedVersatile Video Coding (VVC), and JVET was renamed as Joint Video ExpertTeam.

Intra-prediction modes for luma components in HEVC will now bedescribed. The intra-prediction modes used in HEVC are illustrated inFIG. 1. In HEVC, there may be a total of 35 intra-prediction modes,among which mode 10 may be horizontal mode, mode 26 may be verticalmode, and modes 2, 18 and 34 may be diagonal modes. The intra-predictionmodes may be signaled by three most probable modes (MPMs) and 32remaining modes.

Intra-prediction modes for luma components in VVC will now be described.In the current development of VVC, there may be a total 95intra-prediction modes as shown in FIG. 2, where mode 18 may behorizontal mode, mode 50 may be a vertical mode, and modes 2, 34 and 66may be diagonal modes. Modes −1˜−14 and Modes 67˜80 may be calledWide-Angle Intra-Prediction (WAIP) modes. As shown in FIG. 2, 35intra-prediction modes may be used in HEVC.

Intra-prediction mods for luma components in VVC will now be described.In the current development of VVC, there may be a total 95intra-prediction modes as shown in FIG. 2, where mode 18 may be ahorizontal mode, mode 50 may be a vertical mode, and modes 2, 34 and 66may be diagonal modes. Modes −1˜−14 and Modes 67˜80 may be calledWide-Angle Intra-Prediction (WAIP) modes.

Intra-mode modes for chroma components in VVC will now be described. InVTM, for the chroma components of an intra-PU, an encoder may select thebest chroma prediction mode among 8 modes including Planar, DC,Horizontal, Vertical, a direct copy of the intra-prediction mode (DM)from the luma component, Left and Top Cross-component Linear Mode(LT_CCLM), Left Cross-component Linear Mode (L_CCLM), and TopCross-component Linear Mode (T_CCLM). LT_CCLM, L_CCLM, and T_CCLM may becategorized into the group of Cross-component Linear Mode (CCLM). Adifference between these 3 modes is that different regions ofneighboring samples may be used for deriving the parameters α and β. ForLT_CCLM, both the left and top neighboring samples may be used to derivethe parameters α and β. For L_CCLM, generally only the left neighboringsamples may be used to derive the parameters α and β. For T_CCLM,generally only the top neighboring samples may be used to derive theparameters α and β.

Cross-Component Linear Model (CCLM) prediction modes may be used toreduce the cross-component redundancy, in which chroma samples may bepredicted based on the reconstructed luma samples of the same CU byusing a linear model as follows:

pred_(C)(i,j)=α·rec_(L)′(i,j)+β

where pred_(C)(i,j) represents the predicted chroma samples in a CU andrec_(L)(i,j) represents the downsampled reconstructed luma samples ofthe same CU. Parameters α and β may be derived by a straight-lineequation, also called a max-min method. This computation process may beperformed as part of the decoding process, not just as an encoder searchoperation, so no syntax is necessarily used to convey the α and βvalues.

There are different YUV formats, which is shown in FIGS. 3A-3D. With the4:2:0 format, LM prediction may apply a six-tap interpolation filter toget the down-sampled luma sample corresponding to a chroma sample asshown in FIGS. 3A-3D. In a formulaic way, a down-sampled luma sampleRec′L[x, y] may be calculated from reconstructed luma samples as:

Rec′_(L)[x,y]=(2×Rec_(L)[2x,2y]+2×Rec_(L)[2x,2y+1]+

Rec_(L)[2x−1,2y]+Rec_(L)[2x+1,2y]+

Rec_(L)[2x−1,2y+1]+Rec_(L)[2x+1,2y+1]+4)>>3

The down-sampled luma samples may be used to find the maximum andminimum sample points. The 2 points (couple of Luma and Chroma) (A, B)may be the minimum and maximum values inside the set of neighboring Lumasamples as depicted in FIG. 4.

Where the linear model parameters α and β may be obtained according tothe following equation:

${\alpha = \frac{y_{B} - y_{A}}{x_{B} - x_{A}}}{\beta = {y_{A} - {\alpha x_{A}}}}$

Here, division may be avoided and replaced by a multiplication and ashift. One Look-up Table (LUT) may be used to store the pre-calculatedvalues, and the absolute difference values between maximum and minimumluma samples may be used to specify the entry index of the LUT, and thesize of the LUT may be 512.

It was also proposed that, the absolute difference between maximum andminimum luma sample values within the specified neighboring sampleregions, denoted by diff_Y, may be non-uniform quantized, and thequantized value of the absolute difference may be used to specify theentry index of the CCLM Look-up Table (LUT) such that the size of LUT isreduced. The range of diff_Y is divided into multiple intervals, anddifferent quantization step sizes may be used in different intervals. Inone example, the range of diff_Y may be divided into two intervals, ifdiff_Y is lower than or equal to a threshold, named as Thres 1, one stepsize is used, named as Step_A. Otherwise, another step size may be used,named as Step_B. Consequently, the parameter a in CCLM may be obtainedas follows:

$a = {{{\left( {{diff}_{Y} > {{Thres\_}1}} \right)?{LUT}}\left\lbrack {\frac{{Thres\_}1}{Step\_ A} + \frac{{diff\_ Y} - {{Thres\_}1}}{Step\_ B} - 1} \right\rbrack}:{{LUT}\left\lbrack {{{diff\_}\text{Y/S}{{tep}{\_ A}}} - 1} \right\rbrack}}$

Here, Thres_1, Step_A, and Step_B can be any positive integer, such as1, 2, 3, 4, and so on. Also, Step_A and Step_B are not equal.

To derive the Chroma predictor, as for the current VTM implementation,the multiplication is replaced by an integer operation as the following,where maxY, minY, maxC, and minC denotes the maximum luma sample value,minimum luma sample value, maximum chroma sample value, and minimumchroma sample value respectively. numSampL and numSampT denote thenumber of available left and top neighboring samples respectively. Thefollowing texts are from VVC draft 3 clause 8.2.4.2.8.

The variables a, b, and k are derived as follows:

If numSampL is equal to 0, and numSampT is equal 0, the followingapplies:

-   -   k=0    -   a=0    -   b=1>>(BitDepthC−1)

Otherwise, the following applies:

-   -   shift=(BitDepthC>8) ? BitDepthC−9:0    -   add=shift ? 1<<(shift−1):0    -   diff=(maxY−minY+add)>>shift    -   k=16

If diff is greater than 0, the following applies:

-   -   div=((maxC−minC)*(Floor(2³²/diff)−Floor(2¹⁶/diff)*2¹⁶)*2¹⁵)>>16    -   a=((maxC−minC)*Floor(2¹⁶/diff)+div+add)>>shift        Otherwise, the following applies:    -   a=0    -   b=minC−((a*minY)>>k)

Equation for if diff is greater than 0, can also be simplified asfollows:

a=((maxC−minC)*Floor(2¹⁶/diff)+add)>>shift

After deriving parameters a and b, the chroma predictor is calculated asfollow:

pred_(C)(i,j)=(a·rec_(L)′(i,j))>>S+b

Since the range of variable “diff” is from 1˜512, the value ofFloor(2¹⁶/diff) can be pre-calculated and stored in a look-up table(LUT) with size equal to 512. In addition, the value of diff is used tospecify the entry index of the look-up table.

This computation process is performed as part of the decoding process,not just as an encoder search operation, so no syntax is used to conveythe α and β values.

In a T_CCLM mode, only the above neighboring samples (including 2*Wsamples) are used to calculate the linear model coefficients. In L_CCLMmode, only left neighboring samples (including 2*H samples) are used tocalculate the linear model coefficients. This is illustrated in FIGS.6A-7B.

The CCLM prediction mode also includes prediction between the two chromacomponents, i.e., the Cr component may be predicted from the Cbcomponent. Instead of using the reconstructed sample signal, the CCLMCb-to-Cr prediction may be applied in residual domain. This may beimplemented by adding a weighted reconstructed Cb residual to theoriginal Cr intra-prediction to form the final Cr prediction:

pred_(Cr)*(i,j)=pred_(Cr)(i,j)+α·resi_(Cb)′(i,j)

The CCLM luma-to-chroma prediction mode may be added as one additionalchroma intra-prediction mode. At the encoder side, one more RD costcheck for the chroma components may be added for selecting the chromaintra-prediction mode. When intra-prediction modes other than the CCLMluma-to-chroma prediction mode is used for the chroma components of aCU, CCLM Cb-to-Cr prediction may be used for Cr component prediction.

Multiple Model CCLM (MMLM) is another extension of CCLM. As indicated bythe name, there may be more than one model in MMLM, such as two models.In MMLM, neighboring luma samples and neighboring chroma samples of thecurrent block may be classified into two groups, each group may be usedas a training set to derive a linear model (i.e., a particular α and βare derived for a particular group). Furthermore, the samples of thecurrent luma block may also be classified based on the same rule for theclassification of neighboring luma samples.

FIG. 8 shows an example of classifying the neighboring samples into twogroups. Here, a threshold may be calculated as the average value of theneighboring reconstructed luma samples. A neighboring sample withRec′L[x,y]<=Threshold may be classified into group 1; while aneighboring sample with Rec′L[x,y]>Threshold may be classified intogroup 2

$\left\{ \begin{matrix}{{Pre{d_{C}\left\lbrack {x,y} \right\rbrack}} = {{\alpha_{1} \times {{{Rec}^{\prime}}_{L}\left\lbrack {x,y} \right\rbrack}} + \beta_{1}}} & {{{if}\mspace{14mu}{{{Rec}^{\prime}}_{L}\ \left\lbrack {x,y} \right\rbrack}} \leq {Threshold}} \\{{Pre{d_{C}\left\lbrack {x,y} \right\rbrack}} = {{\alpha_{2} \times \ {{{Rec}^{\prime}}_{L}\left\lbrack {x,y} \right\rbrack}} + \beta_{2}}} & {{{if}\mspace{14mu}{{{Rec}^{\prime}}_{L}\ \left\lbrack {x,y} \right\rbrack}} > {Threshold}}\end{matrix} \right.$

Local Illumination Compensation (LIC) is based on a linear model forillumination changes, using a scaling factor a and an offset b. It isenabled or disabled adaptively for each inter-mode coded coding unit(CU).

When applying LIC for a CU, a least square error method is employed toderive the parameters a and b by using neighboring samples of thecurrent CU and their corresponding reference samples. More specifically,as illustrated in FIGS. 14A and 14B, subsampled (2:1 subsampling)neighboring samples of a current CU 1410 (as shown in portion FIG. A)and the corresponding samples (identified by motion information of thecurrent CU or sub-CU) in a reference picture or block 1420 (as shown inFIG. 14B) are used. IC parameters are derived and applied for eachprediction direction separately.

When a CU is coded with merge mode, an LIC flag is copied fromneighboring blocks, in a way similar to motion information copy in mergemode; otherwise, an LIC flag is signaled for the CU to indicate whetherLIC applies or not.

Despite the above-described advances, there exist issues with the stateof the art. Currently in VTM3.0, with the 4:2:0 YUV format, a 6-tapinterpolation filter may be applied to all the luma samples in aspecified neighboring region, but eventually only the two luma sampleswith maximum and minimum values are used for deriving the CCLMparameters, which increases the decoder complexity without clearbenefits in coding efficiency. Further, in VTM3.0, the interpolationfilter for the luma samples in CCLM only supports 4:2:0 YUV format,however, 4:4:4 and 4:2:2 YUV format is still popular and need to besupported.

Currently in VTM3.0, a size of a look-up table (LUT) for CCLM is 512,i.e., the look-up table has 512 elements and each element is representedby a 16-bit integer, which increases too much decoder memory costwithout clear benefits in coding efficiency. Further, although conceptsof CCLM and LIC are similar, they use different methods to deriveparameters a and b for a linear model, which is not desirable.

Currently in VTM3.0, for large blocks, such as 32×32 chroma blocks, 64samples in a specified neighboring region are used to calculate maximumand minimum values, which increases decoder complexity without clearbenefits in coding efficiency.

Currently in VTM3.0, for large blocks, such as 64×64 blocks, 64 samplesin a specified neighboring region are used to calculate linear modelparameters a and b, which increases decoder complexity without clearbenefits in coding efficiency.

SUMMARY

According to an aspect of the disclosure a method for encoding ordecoding a video sequence may comprise: applying a Cross-ComponentLinear Model (CCLM) to a video sequence, and applying an interpolationfilter in the Cross-Component Linear Model (CCLM), wherein theinterpolation filter may be dependent upon a YUV format of the videosequence.

According to an aspect of the disclosure, in a method described above,wherein in applying the interpolation filter in the Cross-ComponentLinear Model (CCLM), the method further comprises using taps of theinterpolation filter that are dependent upon the YUV format of the videosequence.

According to this aspect of the disclosure, the method may furthercomprise using taps of the interpolation filter used in theCross-Component Linear Model (CCLM) that are in the same form as the YUVformat of the video sequence.

According to an aspect of the disclosure, in a method described above,wherein in applying the interpolation filter in the Cross-ComponentLinear Model (CCLM), the method may further comprise setting the formatof the interpolation filter to be the same for the format of the videosequence when the video sequence includes a YUV format of 4:4:4: or4:2:2, and may comprise setting the format of the interpolation filterto be different than the format of the video sequence when the videosequence includes a YUV format of 4:2:0.

According to an aspect of the disclosure, in a method described above,wherein in applying the interpolation filter in the Cross-ComponentLinear Model (CCLM), the method may further comprise using taps of theinterpolation filter which are different for different YUV formats ofthe video sequence.

According to an aspect of the disclosure, in a method described above,wherein in in applying the interpolation filter in the Cross-ComponentLinear Model (CCLM), the method may further comprise setting theinterpolation filter to be different for top and left neighboring lumareconstructed samples.

According to this aspect of the disclosure, the method may furthercomprise setting the interpolation filter, which is applied to the topand left neighboring luma reconstructed samples to be dependent on theYUV format of the video sequence.

According to an aspect of the disclosure, a method may further comprisesetting a number of lines in top neighboring luma samples and a numberof columns in left neighboring luma samples used in the Cross-ComponentLinear Model (CCLM) to be dependent on the YUV format of the videosequence.

According to this aspect of the disclosure, a method may furthercomprise using one row in a top neighboring region or/and one column ina left neighboring region in the Cross-Component Linear Model (CCLM) forvideo sequences having one of a YUV format of 4:4:4 or 4:2:2.

According to this aspect of the disclosure, a method may furthercomprise using one row in a top neighboring region and/or at least twocolumns in a left neighboring region in the Cross-Component Linear Model(CCLM) for video sequences having a YUV format of 4:2:2.

According to an aspect of the disclosure, a device for encoding ordecoding a video sequence, may comprise: at least one memory configuredto store program code; at least one processor configured to read theprogram code and operate as instructed by the program code, wherein theprogram code may include: first encoding or decoding code configured tocause the at least one processor to apply a Cross-Component Linear Model(CCLM) to a video sequence and apply an interpolation filter in theCross-Component Linear Model (CCLM), wherein the interpolation filtermay be dependent upon a YUV format of the video sequence.

According to an aspect of the disclosure, the first encoding or decodingcode may further comprise code configured to cause the at least oneprocessor to: use taps of the interpolation filter applied, in theCross-Component Linear Model (CCLM), that are dependent upon the YUVformat of the video sequence.

According to this aspect of the disclosure, the first encoding ordecoding code may further comprise code configured to cause the at leastone processor to: use taps of the interpolation filter applied, in theCross-Component Linear Model (CCLM), that are in the same form as theYUV format of the video sequence.

According to an aspect of the disclosure, the first encoding or decodingcode may further comprise code configured to cause the at least oneprocessor to: set the format of the interpolation filter, applied in theCross-Component Linear Model (CCLM), to be the same for the format ofthe video sequence when the video sequence includes a YUV format of4:4:4: or 4:2:2, and set the format of the interpolation filter to bedifferent than the format of the video sequence when the video sequenceincludes a YUV format of 4:2:0.

According to an aspect of the disclosure, the first encoding or decodingcode may further comprise code configured to cause the at least oneprocessor to: use taps of the interpolation filter, applied in theCross-Component Linear Model (CCLM), which are different for differentYUV formats of the video sequence.

According to an aspect of the disclosure, the first encoding or decodingcode may further comprise code configured to cause the at least oneprocessor to: set the interpolation filter, applied in theCross-Component Linear Model (CCLM), to be different for top and leftneighboring luma reconstructed samples.

According to this aspect of the disclosure, the first encoding ordecoding code may further comprise code configured to cause the at leastone processor to: set the interpolation filter, which is applied to thetop and left neighboring luma reconstructed samples, to be dependent onthe YUV format of the video sequence.

According to an aspect of the disclosure, the first encoding or decodingcode may further comprise code configured to cause the at least oneprocessor to: set a number of lines in top neighboring luma samples anda number of columns in left neighboring luma samples used in theCross-Component Linear Model (CCLM) to be dependent on the YUV format ofthe video sequence.

According to this aspect of the disclosure, the first encoding ordecoding code may further comprise code configured to cause the at leastone processor to: use one row in a top neighboring region or/and atleast one column in a left neighboring region in the Cross-ComponentLinear Model (CCLM) for video sequences having one of a YUV format of4:4:4 or 4:2:2.

According to an aspect of the disclosure, a non-transitorycomputer-readable medium storing program code may be provided, theprogram code comprising one or more instructions that, when executed byone or more processors of a device, may cause the one or more processorsto: apply a Cross-Component Linear Model (CCLM) to a video sequence andapply an interpolation filter in the Cross-Component Linear Model(CCLM), wherein the interpolation filter is dependent upon a YUV formatof the video sequence.

While the afore described methods, devices, and non-transitorycomputer-readable mediums have been described individually, thesedescriptions are not intended to suggest any limitation as to the scopeof use or functionality thereof. Indeed these methods, devices, andnon-transitory computer-readable mediums may be combined in otheraspects of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features, the nature, and various advantages of the disclosedsubject matter will be more apparent from the following detaileddescription and the accompanying drawings in which:

FIG. 1 is a diagram of a prediction model in accordance with anembodiment;

FIG. 2 is a diagram of a prediction model in accordance with anembodiment;

FIG. 3A is a diagram a YUV format in accordance with an embodiment;

FIG. 3B is a diagram a YUV format in accordance with an embodiment;

FIG. 3C is a diagram a YUV format in accordance with an embodiment;

FIG. 3D is a diagram a YUV format in accordance with an embodiment;

FIG. 4 is a diagram of different luma values in accordance with anembodiment;

FIG. 5A is a diagram of samples used in cross-component linear modelingin accordance with an embodiment;

FIG. 5B is a diagram of samples used in cross-component linear modelingin accordance with an embodiment;

FIG. 6A is a diagram of samples used in cross-component linear modelingin accordance with an embodiment;

FIG. 6B is a diagram of samples used in cross-component linear modelingin accordance with an embodiment;

FIG. 7A is a diagram of samples used in cross-component linear modelingin accordance with an embodiment;

FIG. 7B is a diagram of samples used in cross-component linear modelingin accordance with an embodiment;

FIG. 8 is an example of a classification using a Multiple Model CCLM inaccordance with an embodiment;

FIG. 9 is a simplified block diagram of a communication system inaccordance with an embodiment;

FIG. 10 is a diagram of a streaming environment in accordance with anembodiment;

FIG. 11 is a block diagram of a video decoder in accordance with anembodiment;

FIG. 12 is a block diagram of a video encoder in accordance with anembodiment;

FIG. 13 is a flowchart of an example process for encoding or decoding avideo sequence in accordance with an embodiment;

FIG. 14A is a diagram of neighboring samples that are used for derivingIllumination Compensation (IC) parameters;

FIG. 14B is a diagram of neighboring samples that are used for derivingIllumination Compensation (IC) parameters;

FIGS. 15, 16, 17, 18, 19, 20, 21, 22, 23 and 24 are diagrams of acurrent Coding Unit (CU) and a subset of neighboring reconstructedsamples of the current CU that are used to calculate maximum and minimumsample values, according to embodiments;

FIGS. 25 and 26 are diagrams of a chroma block and selected neighboringsamples of the chroma block that are used to calculate maximum andminimum sample values, according to embodiments;

FIGS. 27 and 28 are diagrams of positions of filters for luma samples,according to embodiments;

FIGS. 29 and 30 are diagrams of a current CU and a neighboring samplepair of the current CU that are used to calculate parameters in a linearmodel prediction mode, according to embodiments;

FIGS. 31, 32, 33, 34, 35 and 36 are diagrams of diagrams of a block andselected neighboring samples of the block that are used to calculatelinear model parameters, according to embodiments; and

FIG. 37 is a diagram of a computer system in accordance with anembodiment.

DETAILED DESCRIPTION

FIG. 9 illustrates a simplified block diagram of a communication system(400) according to an embodiment of the present disclosure. Thecommunication system (400) may include at least two terminals (410-420)interconnected via a network (450). For unidirectional transmission ofdata, a first terminal (410) may code video data at a local location fortransmission to the other terminal (420) via the network (450). Thesecond terminal (420) may receive the coded video data of the otherterminal from the network (450), decode the coded data and display therecovered video data. Unidirectional data transmission may be common inmedia serving applications and the like.

FIG. 9 illustrates a second pair of terminals (430, 440) provided tosupport bidirectional transmission of coded video that may occur, forexample, during videoconferencing. For bidirectional transmission ofdata, each terminal (430, 440) may code video data captured at a locallocation for transmission to the other terminal via the network (450).Each terminal (430, 440) also may receive the coded video datatransmitted by the other terminal, may decode the coded data and maydisplay the recovered video data at a local display device.

In FIG. 9, the terminals (410-440) may be illustrated as servers,personal computers and smart phones but the principles of the presentdisclosure are not so limited. Embodiments of the present disclosurefind application with laptop computers, tablet computers, media playersand/or dedicated video conferencing equipment. The network (450)represents any number of networks that convey coded video data among theterminals (410-440), including for example wireline and/or wirelesscommunication networks. The communication network (450) may exchangedata in circuit-switched and/or packet-switched channels. Representativenetworks include telecommunications networks, local area networks, widearea networks and/or the Internet. For the purposes of the presentdiscussion, the architecture and topology of the network (450) may beimmaterial to the operation of the present disclosure unless explainedherein below.

FIG. 10 illustrates, as an example for an application for the disclosedsubject matter, the placement of a video encoder and decoder in astreaming environment. The disclosed subject matter can be equallyapplicable to other video enabled applications, including, for example,video conferencing, digital TV, storing of compressed video on digitalmedia including CD, DVD, memory stick and the like, and so on.

A streaming system may include a capture subsystem (513), that caninclude a video source (501), for example a digital camera, creating,for example, an uncompressed video sample stream (502). That samplestream (502), depicted as a bold line to emphasize a high data volumewhen compared to encoded video bitstreams, can be processed by anencoder (503) coupled to the camera (501). The encoder (503) can includehardware, software, or a combination thereof to enable or implementaspects of the disclosed subject matter as described in more detailbelow. The encoded video bitstream (504), depicted as a thin line toemphasize the lower data volume when compared to the sample stream, canbe stored on a streaming server (505) for future use. One or morestreaming clients (506, 508) can access the streaming server (505) toretrieve copies (507, 509) of the encoded video bitstream (504). Aclient (506) can include a video decoder (510) which decodes theincoming copy of the encoded video bitstream (507) and creates anoutgoing video sample stream (511) that can be rendered on a display(512) or other rendering device (not depicted). In some streamingsystems, the video bitstreams (504, 507, 509) can be encoded accordingto certain video coding/compression standards. Examples of thosestandards include H.265 HEVC. Under development is a video codingstandard informally known as Versatile Video Coding (VVC). The disclosedsubject matter may be used in the context of VVC.

FIG. 11 may be a functional block diagram of a video decoder (510)according to an embodiment of the present invention.

A receiver (610) may receive one or more codec video sequences to bedecoded by the decoder (610); in the same or another embodiment, onecoded video sequence at a time, where the decoding of each coded videosequence is independent from other coded video sequences. The codedvideo sequence may be received from a channel (612), which may be ahardware/software link to a storage device which stores the encodedvideo data. The receiver (610) may receive the encoded video data withother data, for example, coded audio data and/or ancillary data streams,that may be forwarded to their respective using entities (not depicted).The receiver (610) may separate the coded video sequence from the otherdata. To combat network jitter, a buffer memory (615) may be coupled inbetween receiver (610) and entropy decoder/parser (620) (“parser”henceforth). When receiver (610) is receiving data from a store/forwarddevice of sufficient bandwidth and controllability, or from anisosychronous network, the buffer (615) may not be needed, or can besmall. For use on best effort packet networks such as the Internet, thebuffer (615) may be required, can be comparatively large and canadvantageously of adaptive size.

The video decoder (510) may include a parser (620) to reconstructsymbols (621) from the entropy coded video sequence. Categories of thosesymbols include information used to manage operation of the decoder(510), and potentially information to control a rendering device such asa display (512) that is not an integral part of the decoder but can becoupled to it, as was shown in FIG. 11. The control information for therendering device(s) may be in the form of Supplementary EnhancementInformation (SEI messages) or Video Usability Information (VUI)parameter set fragments (not depicted). The parser (620) mayparse/entropy-decode the coded video sequence received. The coding ofthe coded video sequence can be in accordance with a video codingtechnology or standard, and can follow principles well known to a personskilled in the art, including variable length coding, Huffman coding,arithmetic coding with or without context sensitivity, and so forth. Theparser (620) may extract from the coded video sequence, a set ofsubgroup parameters for at least one of the subgroups of pixels in thevideo decoder, based upon at least one parameters corresponding to thegroup. Subgroups can include Groups of Pictures (GOPs), pictures, tiles,slices, macroblocks, Coding Units (CUs), blocks, Transform Units (TUs),Prediction Units (PUs) and so forth. The entropy decoder/parser may alsoextract from the coded video sequence information such as transformcoefficients, quantizer parameter (QP) values, motion vectors, and soforth.

The parser (620) may perform entropy decoding/parsing operation on thevideo sequence received from the buffer (615), so to create symbols(621). The parser (620) may receive encoded data, and selectively decodeparticular symbols (621). Further, the parser (620) may determinewhether the particular symbols (621) are to be provided to a MotionCompensation Prediction unit (653), a scaler/inverse transform unit(651), an Intra-Prediction unit (652), or a loop filter unit (658).

Reconstruction of the symbols (621) can involve multiple different unitsdepending on the type of the coded video picture or parts thereof (suchas: inter and intra-picture, inter and intra-block), and other factors.Which units are involved, and how, can be controlled by the subgroupcontrol information that was parsed from the coded video sequence by theparser (620). The flow of such subgroup control information between theparser (620) and the multiple units below is not depicted for clarity.

Beyond the functional blocks already mentioned, decoder (510) can beconceptually subdivided into a number of functional units as describedbelow. In a practical implementation operating under commercialconstraints, many of these units interact closely with each other andcan, at least partly, be integrated into each other. However, for thepurpose of describing the disclosed subject matter, the conceptualsubdivision into the functional units below is appropriate.

A first unit is the scaler/inverse transform unit (651). Thescaler/inverse transform unit (651) receives quantized transformcoefficient as well as control information, including which transform touse, block size, quantization factor, quantization scaling matrices,etc. as symbol(s) (621) from the parser (620). It can output blockscomprising sample values, that can be input into aggregator (655).

In some cases, the output samples of the scaler/inverse transform (651)can pertain to an intra-coded block; that is: a block that is not usingpredictive information from previously reconstructed pictures, but canuse predictive information from previously reconstructed parts of thecurrent picture. Such predictive information can be provided by anintra-picture prediction unit (652). In some cases, the intra-pictureprediction unit (652) generates a block of the same size and shape ofthe block under reconstruction, using surrounding already reconstructedinformation fetched from the current (partly reconstructed) picture(656). The aggregator (655), in some cases, adds, on a per sample basis,the prediction information the intra-prediction unit (652) has generatedto the output sample information as provided by the scaler/inversetransform unit (651).

In other cases, the output samples of the scaler/inverse transform unit(651) can pertain to an inter coded, and potentially motion compensatedblock. In such a case, a Motion Compensation Prediction unit (653) canaccess reference picture memory (657) to fetch samples used forprediction. After motion compensating the fetched samples in accordancewith the symbols (621) pertaining to the block, these samples can beadded by the aggregator (655) to the output of the scaler/inversetransform unit (in this case called the residual samples or residualsignal) so to generate output sample information. The addresses withinthe reference picture memory form where the motion compensation unitfetches prediction samples can be controlled by motion vectors,available to the motion compensation unit in the form of symbols (621)that can have, for example X, Y, and reference picture components.Motion compensation also can include interpolation of sample values asfetched from the reference picture memory when sub-sample exact motionvectors are in use, motion vector prediction mechanisms, and so forth.

The output samples of the aggregator (655) can be subject to variousloop filtering techniques in the loop filter unit (658). Videocompression technologies can include in-loop filter technologies thatare controlled by parameters included in the coded video bitstream andmade available to the loop filter unit (658) as symbols (621) from theparser (620), but can also be responsive to meta-information obtainedduring the decoding of previous (in decoding order) parts of the codedpicture or coded video sequence, as well as responsive to previouslyreconstructed and loop-filtered sample values.

The output of the loop filter unit (658) can be a sample stream that canbe output to the render device (512) as well as stored in the referencepicture memory (656) for use in future inter-picture prediction.

Certain coded pictures, once fully reconstructed, can be used asreference pictures for future prediction. Once a coded picture is fullyreconstructed and the coded picture has been identified as a referencepicture (by, for example, parser (620)), the current reference picture(656) can become part of the reference picture buffer (657), and a freshcurrent picture memory can be reallocated before commencing thereconstruction of the following coded picture.

The video decoder (510) may perform decoding operations according to apredetermined video compression technology that may be documented in astandard, such as H.265 HEVC. The coded video sequence may conform to asyntax specified by the video compression technology or standard beingused, in the sense that it adheres to the syntax of the videocompression technology or standard, as specified in the videocompression technology document or standard and specifically in theprofiles document therein. Also necessary for compliance can be that thecomplexity of the coded video sequence is within bounds as defined bythe level of the video compression technology or standard. In somecases, levels restrict the maximum picture size, maximum frame rate,maximum reconstruction sample rate (measured in, for example megasamplesper second), maximum reference picture size, and so on. Limits set bylevels can, in some cases, be further restricted through HypotheticalReference Decoder (HRD) specifications and metadata for HRD buffermanagement signaled in the coded video sequence.

In an embodiment, the receiver (610) may receive additional (redundant)data with the encoded video. The additional data may be included as partof the coded video sequence(s). The additional data may be used by thevideo decoder (510) to properly decode the data and/or to moreaccurately reconstruct the original video data. Additional data can bein the form of, for example, temporal, spatial, or signal-to-noise ratio(SNR) enhancement layers, redundant slices, redundant pictures, forwarderror correction codes, and so on.

FIG. 12 may be a functional block diagram of a video encoder (503)according to an embodiment of the present disclosure.

The encoder (503) may receive video samples from a video source (501)(that is not part of the encoder) that may capture video image(s) to becoded by the encoder (503).

The video source (501) may provide the source video sequence to be codedby the encoder (503) in the form of a digital video sample stream thatcan be of any suitable bit depth (for example: 8 bit, 10 bit, 12 bit, .. . ), any colorspace (for example, BT.601 Y CrCB, RGB, . . . ) and anysuitable sampling structure (for example Y CrCb 4:2:0, Y CrCb 4:4:4). Ina media serving system, the video source (501) may be a storage devicestoring previously prepared video. In a videoconferencing system, thevideo source (503) may be a camera that captures local image informationas a video sequence. Video data may be provided as a plurality ofindividual pictures that impart motion when viewed in sequence. Thepictures themselves may be organized as a spatial array of pixels,wherein each pixel can comprise one or more samples depending on thesampling structure, color space, etc. in use. A person skilled in theart can readily understand the relationship between pixels and samples.The description below focuses on samples.

According to an embodiment, the encoder (503) may code and compress thepictures of the source video sequence into a coded video sequence (743)in real time or under any other time constraints as required by theapplication. Enforcing appropriate coding speed is one function ofController (750). Controller (750) controls other functional units asdescribed below and is functionally coupled to these units. The couplingis not depicted for clarity. Parameters set by controller can includerate control related parameters (picture skip, quantizer, lambda valueof rate-distortion optimization techniques, etc.), picture size, groupof pictures (GOP) layout, maximum motion vector search range, and soforth. A person skilled in the art can readily identify other functionsof controller (750) as they may pertain to video encoder (503) optimizedfor a certain system design.

Some video encoders operate in what a person skilled in the art readilyrecognizes as a “coding loop.” As an oversimplified description, acoding loop can consist of the encoding part of an encoder (730)(“source coder” henceforth) (responsible for creating symbols based onan input picture to be coded, and a reference picture(s)), and a (local)decoder (733) embedded in the encoder (503) that reconstructs thesymbols to create the sample data that a (remote) decoder also wouldcreate (as any compression between symbols and coded video bit stream islossless in the video compression technologies considered in thedisclosed subject matter). That reconstructed sample stream is input tothe reference picture memory (734). As the decoding of a symbol streamleads to bit-exact results independent of decoder location (local orremote), the reference picture buffer content is also bit exact betweenlocal encoder and remote encoder. In other words, the prediction part ofan encoder “sees” as reference picture samples exactly the same samplevalues as a decoder would “see” when using prediction during decoding.This fundamental principle of reference picture synchronicity (andresulting drift, if synchronicity cannot be maintained, for examplebecause of channel errors) is well known to a person skilled in the art.

The operation of the “local” decoder (733) can be the same as of a“remote” decoder (510), which has already been described in detail abovein conjunction with FIG. 10. Briefly referring also to FIG. 10, however,as symbols are available and en/decoding of symbols to a coded videosequence by entropy coder (745) and parser (620) can be lossless, theentropy decoding parts of decoder (510), including channel (612),receiver (610), buffer (615), and parser (620) may not be fullyimplemented in local decoder (733).

An observation that can be made at this point is that any decodertechnology except the parsing/entropy decoding that is present in adecoder also necessarily needs to be present, in substantially identicalfunctional form, in a corresponding encoder. The description of encodertechnologies can be abbreviated as they are the inverse of thecomprehensively described decoder technologies. Only in certain areas amore detail description is required and provided below.

As part of its operation, the source coder (730) may perform motioncompensated predictive coding, which codes an input frame predictivelywith reference to one or more previously-coded frames from the videosequence that were designated as “reference frames.” In this manner, thecoding engine (732) codes differences between pixel blocks of an inputframe and pixel blocks of reference frame(s) that may be selected asprediction reference(s) to the input frame.

The local video decoder (733) may decode coded video data of frames thatmay be designated as reference frames, based on symbols created by thesource coder (730). Operations of the coding engine (732) mayadvantageously be lossy processes. When the coded video data may bedecoded at a video decoder (not shown in FIG. 11), the reconstructedvideo sequence typically may be a replica of the source video sequencewith some errors. The local video decoder (733) replicates decodingprocesses that may be performed by the video decoder on reference framesand may cause reconstructed reference frames to be stored in thereference picture cache (734). In this manner, the encoder (503) maystore copies of reconstructed reference frames locally that have commoncontent as the reconstructed reference frames that will be obtained by afar-end video decoder (absent transmission errors).

The predictor (735) may perform prediction searches for the codingengine (732). That is, for a new frame to be coded, the predictor (735)may search the reference picture memory (734) for sample data (ascandidate reference pixel blocks) or certain metadata such as referencepicture motion vectors, block shapes, and so on, that may serve as anappropriate prediction reference for the new pictures. The predictor(735) may operate on a sample block-by-pixel block basis to findappropriate prediction references. In some cases, as determined bysearch results obtained by the predictor (735), an input picture mayhave prediction references drawn from multiple reference pictures storedin the reference picture memory (734).

The controller (750) may manage coding operations of the video coder(730), including, for example, setting of parameters and subgroupparameters used for encoding the video data.

Output of all aforementioned functional units may be subjected toentropy coding in the entropy coder (745). The entropy coder translatesthe symbols as generated by the various functional units into a codedvideo sequence, by loss-less compressing the symbols according totechnologies known to a person skilled in the art as, for exampleHuffman coding, variable length coding, arithmetic coding, and so forth.

The transmitter (740) may buffer the coded video sequence(s) as createdby the entropy coder (745) to prepare it for transmission via acommunication channel (760), which may be a hardware/software link to astorage device which would store the encoded video data. The transmitter(740) may merge coded video data from the video coder (730) with otherdata to be transmitted, for example, coded audio data and/or ancillarydata streams (sources not shown).

The controller (750) may manage operation of the encoder (503). Duringcoding, the controller (750) may assign to each coded picture a certaincoded picture type, which may affect the coding techniques that may beapplied to the respective picture. For example, pictures often may beassigned as one of the following frame types:

An Intra-Picture (I picture) may be one that may be coded and decodedwithout using any other frame in the sequence as a source of prediction.Some video codecs allow for different types of Intra-pictures,including, for example Independent Decoder Refresh Pictures. A personskilled in the art is aware of those variants of I pictures and theirrespective applications and features.

A Predictive picture (P picture) may be one that may be coded anddecoded using intra-prediction or inter prediction using at most onemotion vector and reference index to predict the sample values of eachblock.

A Bi-directionally Predictive Picture (B Picture) may be one that may becoded and decoded using intra-prediction or inter prediction using atmost two motion vectors and reference indices to predict the samplevalues of each block. Similarly, multiple-predictive pictures can usemore than two reference pictures and associated metadata for thereconstruction of a single block.

Source pictures commonly may be subdivided spatially into a plurality ofsample blocks (for example, blocks of 4×4, 8×8, 4×8, or 16×16 sampleseach) and coded on a block-by-block basis. Blocks may be codedpredictively with reference to other (already coded) blocks asdetermined by the coding assignment applied to the blocks' respectivepictures. For example, blocks of I pictures may be codednon-predictively or they may be coded predictively with reference toalready coded blocks of the same picture (spatial prediction orintra-prediction). Pixel blocks of P pictures may be codednon-predictively, via spatial prediction or via temporal prediction withreference to one previously coded reference pictures. Blocks of Bpictures may be coded non-predictively, via spatial prediction or viatemporal prediction with reference to one or two previously codedreference pictures.

The video coder (503) may perform coding operations according to apredetermined video coding technology or standard, such as H.265 HEVC.In its operation, the video coder (503) may perform various compressionoperations, including predictive coding operations that exploit temporaland spatial redundancies in the input video sequence. The coded videodata, therefore, may conform to a syntax specified by the video codingtechnology or standard being used.

In an embodiment, the transmitter (740) may transmit additional datawith the encoded video. The video coder (730) may include such data aspart of the coded video sequence. Additional data may comprisetemporal/spatial/SNR enhancement layers, other forms of redundant datasuch as redundant pictures and slices, Supplementary EnhancementInformation (SEI) messages, Visual Usability Information (VUI) parameterset fragments, and so on.

The present disclosure is directed to various methods for aCross-Component Linear Model Prediction Mode.

As described above, in this application, CCLM may refer to any variantsof a Cross-Component Linear Model, such as LT_CCLM, T_CCLM, L_CCLM,MMLM, LT_MMLM, L_MMLM, and T_MMLM. Further, in this application, asmoothing filter may be defined as a linear filter with an odd number oftaps, and filter coefficients thereof may be symmetric. For example, a3-tap filter [1, 2, 1] and 5-tap filter [1, 3, 8, 3, 1] are bothsmoothing filters. An interpolation filter may be defined as a linearfilter to generate a fractional position sample using integer positionsamples.

It is proposed that, an interpolation filter used for CCLM may beapplied to a subset of the neighboring luma reconstructed samples in thespecified region.

In one aspect, the specified neighboring sample region may be a leftneighboring sample region used for L_CCLM and L_MMLM, or a topneighboring sample region used for T_CCLM and T_MMLM, or a top and aleft neighboring sample region used for LT_CCLM and LT_MMLM.

In another aspect, the reconstructed luma samples in the specifiedregion may be directly used to find the maximum and minimum values.After obtaining the maximum and minimum luma sample values, aninterpolation (or smoothing) filter may be (e.g. only) applied to thesetwo samples.

In another aspect, after obtaining the maximum and minimum luma samplevalues, the co-located positions of the minimum and maximum luma samplesin a chroma neighboring sample region may be recorded as (x_min, y_min)and (x_max, y_max), respectively, and a down-sample filter may beapplied to the co-located luma samples for chroma samples with position(x_min, y_min) and (x_max, y_max).

In another aspect, for a 4:2:0 YUV format, the down-sample filter may beapplied as follows: (x, y) in the following equation may be replaced by(x_min, y_min) or (x_max, y_max), rec_(L)′^((x,y)) may denote thedown-sampled luma sample values, rec_(L) ^((kx,ky)) may denote theconstructed luma samples at the specified neighboring position (k·x,k·y), k may be a positive integer, such as 1, 2, 3, or 4.

rec_(L)′^((x,y))=(2×rec_(L) ^((kx,ky))+2×rec_(L) ^((kx,ky+1))+rec_(L)^((kx−1,ky))+rec_(L) ^((kx−1,ky+1))+rec_(L) ^((kx−1,ky))+rec_(L)^((kx−1,ky+1)))>>3

In another aspect, for a 4:2:0 YUV format, the co-located positions in aluma sample region for one chroma sample with position (x, y) may be anyone of the following 6 positions (k*x, k*y), (k*x−1, k*y), (k*x+1, k*y),(k*x−1, k*y+1), (k*x, k*y+1), and (k*x+1, k*y+1). Where k is a positiveinteger, such as 1, 2, 3, or 4.

In another aspect, for 4:4:4 and 4:2:2 YUV formats, the interpolationfilter described above can be used here.

In another aspect, the reconstructed luma samples in the specifiedregion may be directly used to find the maximum and minimum values.

In another aspect, for a LT_CCLM mode, the neighboring luma samples inthe first top reference row (as shown in FIGS. 6A-6B) and second leftreference column (as shown in FIGS. 7A-7B) may be directly used to findthe maximum and minimum values.

In another aspect, for a LT_CCLM mode, half of the neighboring lumasamples in the first and second top reference row (as shown in FIGS.6A-6B) and the samples in second left reference column (as shown inFIGS. 7A-7B) may be directly used to find the maximum and minimumvalues.

In another aspect, for a T_CCLM mode, the neighboring luma samples inthe first top reference row may be directly used to find the maximum andminimum values.

In another aspect, for a T_CCLM mode, half of the neighboring lumasamples in the first and second top reference row may be directly usedto find the maximum and minimum values.

In another aspect, for a L_CCLM mode, the neighboring luma samples inthe second left reference column may be directly used to find themaximum and minimum values.

In another aspect, when an interpolation filter is used (e.g. only used)on partial of the neighboring luma samples, a downsampling filterdifferent from the one used in current CCLM, i.e., the 6-tap[1,2,1;1,2,1]/8 filter, may be applied. For example, the 10-tap filtermay be [1,4,6,4,1; 1,4,6,4,1]/16

In another aspect, the downsampling filter may be an 8-tap filter,wherein the number of filter taps in the first (nearest to the currentblock) reference row (as shown in FIGS. 6A-6B) may be different from theother (farther to the current block) reference column (as shown in FIGS.7A-7B). For example, the 8-tap filter may be [1,2,1; 1,2,6,2,1]/16, andthe number of filter taps for the first reference row (or column) may be5, whereas the number of taps for the second reference row (or column)may be 3.

In another aspect it is proposed that, the reconstructed chroma samplesin the specified region may be used to find the maximum and minimumvalues.

In one aspect, after obtaining the maximum and minimum chroma samplevalues, interpolation (or smoothing)filter may be applied (e.g. onlyapplied) to the co-located luma samples of these two chroma samples. Forexample, for a 4:2:0 YUV format, the interpolation (or smoothing) filterdescribed above can be used. As another example, for a 4:2:0 YUV format,the co-located positions for one chroma sample with position (x,y)described above can be used here. In yet another example, for 4:4:4 and4:2:2 YUV formats, the interpolation filter in the following descriptionmay be used here.

According to one aspect, it is proposed that, the interpolation filter(or smoothing filter) used in CCLM may be dependent on the YUV format,such as 4:4:4, 4:2:2, or 4:2:0 YUV format.

FIG. 13 is a flowchart of an example process (800) for encoding ordecoding a video sequence. In some implementations, one or more processblocks of FIG. 13 may be performed by decoder (510). In someimplementations, one or more process blocks of FIG. 13 may be performedby another device or a group of devices separate from or includingdecoder (510), such as encoder (503).

As shown in FIG. 13, process (800) may comprise encoding or decoding avideo sequence (810).

As further shown in FIG. 13, process (800) may further comprise applyinga Cross-Component Linear Model (CCLM) to the video sequence (820).

As further shown in FIG. 13, process (800) may further comprise applyingan interpolation filter in the Cross-Component Linear Model (CCLM),wherein the interpolation filter is dependent upon a YUV format of thevideo sequence (830).

Although FIG. 13 shows example blocks of process (800), in someimplementations, process (800) may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 13. Additionally, or alternatively, two or more of theblocks of process (800) may be performed in parallel.

According to another aspect, the taps of interpolation (or smoothing)filter used in CCLM may be dependent on YUV formats.

In another aspect, the taps of the interpolation (or smoothing) filterused in CCLM may be the same for the same YUV format. For example, thetaps of an interpolation filter used in CCLM may be the same for a 4:2:0YUV format.

In another aspect, the interpolation or smoothing filter for 4:4:4 and4:2:2 YUV formats is the same, but different with a filter for 4:2:0 YUVformat. For example, a 3-tap (1,2,1) filter may be used for 4:4:4 and4:2:2 YUV formats whereas a 6-tap (1,2,1;1,2,1) filter may be used for a4:2:0 YUV format. As another example, no down-sample (or smoothing)filter may be used for 4:4:4 and 4:2:2 YUV formats, whereas a 6-tap(1,2,1,1,2,1) or a 3-tap (1,2,1) filter may be used for a 4:2:0 YUVformat.

According to another aspect, the taps of an interpolation (or smoothing)filter used in CCLM may be different for different YUV formats. Forexample, a 5-tap (1,1,4,1,1) filter may be used for a 4:4:4 YUV format,a (1,2,1) filter may be used for a 4:2:2 YUV format, and a 6 tap(1,2,1,1,2,1) filter may be used for a 4:2:0 YUV format.

In another aspect, the interpolation (or smoothing) filter may bedifferent for top and left neighboring luma in reconstructed samples.

According to another aspect, the interpolation (or smoothing) filter fortop and left neighboring luma reconstructed samples may be dependent onthe YUV formats.

According to another aspect, for a 4:2:2 YUV format, the interpolation(or smoothing) filter may be different for top and left neighboringsamples. For example, for a 4:2:2 YUV format, (1,2,1) the filter may beapplied to top neighboring luma samples and no interpolation (orsmoothing) filter is applied to left neighboring luma samples.

According to another aspect, the number of lines in top neighboring lumasamples and the number of columns in left neighboring luma samples usedin CCLM may be dependent on the YUV format. For example, only one row ina top neighboring region or/and one column in a left neighboring regionmay be used for 4:4:4 YUV or 4:2:2 YUV format. As another example, onerow in a top neighboring region and/or 3 (or 2) columns in a leftneighboring region may be used for a 4:2:2 YUV format.

In another aspect, it is proposed that, the absolute difference betweenmaximum and minimum luma sample values within the specified neighboringsample regions, denoted by diff_Y, is non-uniform quantized, and thequantized value of the absolute difference may be used to specify theentry index of the CCLM Look-up Table (LUT) such that the size of LUT isreduced. The range of diff_Y may be divided into two intervals, ifdiff_Y is lower than or equal to a threshold, named as Thres_1, one stepsize may be used, named as Step_A. Otherwise, another step size is used,named as Step_B. Consequently, the parameter a in CCLM may be obtainedas follows:

$a = {{{\left( {{diff}_{Y} > {{Thres\_}1}} \right)?{LUT}}\left\lbrack {\frac{{Thres\_}1}{Step\_ A} + \frac{{diff\_ Y} - {{Thres\_}1}}{Step\_ B} - 1} \right\rbrack}:{{LUT}\left\lbrack {{{diff\_}\text{Y/S}{{tep}{\_ A}}} - 1} \right\rbrack}}$

Here in this aspect, Thres_1 may be set to equal to 64, Step_A may beset equal to 1, and Step_B may be set equal to 8

Now, below will be described a modified CCLM parameter derivationprocess on top of VVC draft 3 clause 8.2.4.2.8.

According to another aspect, in the equation immediately above, thevariables a, b, and k may be derived as follows:

-   -   If numSampL is equal to 0, and numSampT is equal to 0, the        following applies:    -   k=0    -   a=0    -   b=1<<(BitDepth_(C)−1)    -   Otherwise, the following applies:        -   shift=(BitDepth_(C)>8) ? BitDepth_(C)—9:0        -   add=shift ? 1<<(shift−1):0        -   diff=(maxY−minY+add)>>shift        -   k=16            -   If diff is greater than 0, the following applies:            -   diff=(diff>64) ? (56+(diff>>3)):diff                -   a=((maxC−minC)*g_aiLMDivTableHigh[diff−1]+add)>>shift            -   Otherwise, the following applies:                -   a=0            -   b=min_(C)−((a*minY)>>k)

Here, the prediction samples predSamples[x][y] with x=0 . . . nTbW−1,y=0 . . . nTbH−1 may be derived as follows:

predSamples[x][y]=Clip1C(((pDsY[x][y]*a)>>k)+b)

Further, the simplified CCLM look-up table is shown below:

int g_aiLMDivTableHigh[ ]={

-   -   65536, 32768, 21845, 16384, 13107, 10922, 9362, 8192, 7281,        6553, 5957, 5461, 5041, 4681, 4369, 4096,    -   3855, 3640, 3449, 3276, 3120, 2978, 2849, 2730, 2621, 2520,        2427, 2340, 2259, 2184, 2114, 2048,    -   1985, 1927, 1872, 1820, 1771, 1724, 1680, 1638, 1598, 1560,        1524, 1489, 1456, 1424, 1394, 1365,    -   1337, 1310, 1285, 1260, 1236, 1213, 1191, 1170, 1149, 1129,        1110, 1092, 1074, 1057, 1040, 1024,    -   910, 819, 744, 682, 630, 585, 546, 512, 481, 455, 431, 409, 390,        372, 356, 341,    -   327, 315, 303, 292, 282, 273, 264, 256, 248, 240, 234, 227, 221,        215, 210, 204,    -   199, 195, 190, 186, 182, 178, 174, 170, 167, 163, 160, 157, 154,        151, 148, 146,    -   143, 141, 138, 136, 134, 132, 130, 128,};

In embodiments, an absolute difference between maximum and minimum lumasample values within specified neighboring sample regions, denoted bydiff_Y, is non-uniform quantized, and a quantized value of the absolutedifference is used to specify an entry index of a CCLM Look-up Table(LUT) such that a size of LUT is reduced.

For example, a specified neighboring sample region may be a leftneighboring sample region used for L_CCLM and L_MMLM, or a topneighboring sample region used for T_CCLM and T_MMLM, or top and leftneighboring sample regions used for TL_CCLM and TL MMLM.

In another example, a range of diff_Y is divided into multipleintervals, and different quantization step sizes are used in differentintervals. In first embodiments, the range of diff_Y is divided into twointervals; if diff_Y is lower than or equal to a threshold, named asThres_1, one step size is used, named as Step_A. Otherwise, another stepsize is used, named as Step_B. Consequently, a parameter a in CCLM maybe obtained as follows:

$a = {{{\left( {{diff}_{Y} > {{Thres\_}1}} \right)?{LUT}}\left\lbrack {{{diff\_}\text{Y/S}{{tep}{\_ A}}} - 1} \right\rbrack}:{{LU}{T\left\lbrack {\frac{{Thres\_}1}{Step\_ A} + \frac{{diff\_ Y} - {{Thres\_}1}}{Step\_ B} - 1} \right\rbrack}}}$

Each of Thres_1, Step_A, and Step_B can be any positive integer, such as1, 2, 3, 4, and so on. Step_A and Step_B are not equal. In an example,Thres_1 is set equal to 32, or 48, or 64. In another example, Thres_1 isset equal to 64, Step_A is set equal to 1, and Step_B is set equal to 8.In another example, Thres_1 is set equal to 64, Step_A is set equal to 2(or 1), and Step_B is set equal to 8 (or 4). In another example, a valueof Step_A is smaller than a value of Step_B.

In second embodiments, the range of diff_Y is divided into threeintervals by two thresholds, namely Thres_1 and Thres_2. If diff_Y islower than or equal to Thres_1, one step size is used, namely Step_A;otherwise, if diff_Y is lower than or equal to Thres_2, another stepsize is used, namely Step_B; otherwise, a third step size is used,namely Step_C. Each of Thres_1, Thres_2, Step_A, Step_B, and Step_C canbe any positive integer, such as 1, 2, 3, 4, and so on. Thres_1 is lessthan Thres_2. Step_A, Step_B, and Step_C are not equal. In an example,Thres_1 and Thres_2 are set equal to 64 and 256, respectively. Inanother example, Step_A, Step_B, and Step_C are set equal to 2 (or 1), 8(or 4), and 32 (or 16), respectively.

In third embodiments, threshold values that are used for specifyingintervals (such as the Thres_1 and Thres_2) are powers of 2, e.g., 2, 4,8, 16, 32, 64, 128, 256, 512, 1024.

In further embodiments, LIC re-uses a max-min method in CCLM to deriveparameters a and b.

For example, LIC re-uses a Look-up table in CCLM to avoid a divisionoperation.

In another example, LIC uses a same neighboring reconstructed sampleregion as that used for a luma component in TL_CCLM to derive parametersa and b.

In another example, LIC re-uses a same down-sample method in CCLM tofilter neighboring reconstructed samples.

In further embodiments, only a subset of neighboring reconstructedsamples in a specified region is used to calculate maximum and minimumsample values for a max-min method in CCLM prediction mode.

For example, only 4 samples in a neighboring reconstructed sample regionare used to calculate maximum and minimum sample values regardless of ablock size of a current block. In this example, a position of selected 4samples are fixed and shown in FIG. 15. To be specific, with respect toa current CU 1510, only sample values of neighboring reconstructedsamples at position A, B, C, and D are used for calculating maximum andminimum sample values.

In another example, for TL_CCLM mode, only left (or above) neighboringsamples are used to calculate a minimum sample value and only above (orleft) neighboring samples are used to calculate a maximum sample value.

In another example, instead of finding maximum and minimum sample valuesfor a max-min method in CCLM prediction mode, a mean value of top lumaand chroma reference samples, namely top_mean_luma, top_mean_chroma, anda mean values of left luma and chroma reference samples, namely,left_mean_luma, left_mean_chroma, may be used to derive CCLM parameters.

In the following embodiments, referring to FIG. 16, when a top-leftsample and a bottom-left sample of a current CU 1610 are used, theseblocks may be replaced with their direct neighboring samples that arelocated outside a range of an above and left sample of the current CU1610. For example, in FIG. 16, samples A and B may be used.

In another example, in FIG. 17, samples A, B and C may be used in acurrent CU 1710.

In the following embodiments, the chroma sample (in U or V component)together with its co-located luma sample is named as a sample pair.Alternatively, for LIC, a neighboring sample of a current block togetherwith a neighboring sample in a co-located position of a reference blockis named as a sample pair, so the samples included in the sample paircomes from a same color component (luma, U or V).

In the following embodiments, for CCLM or any variant/extension of CCLMmode, a block size may be of a chroma block. For LIC mode or any variantof LIC, a block size may be of a current block of a current colorcomponent (luma, U or V).

In embodiments, up to N neighboring reconstructed sample pairs in aspecified region is used to calculate maximum and minimum sample valuesfor a max-min method in CCLM prediction mode, and N is a positiveinteger such as 4, 8, or 16.

For example, for an N×2 or 2×N chroma block, only one neighboring samplepair from left and one neighboring sample pair from above are used tocalculate maximum and minimum sample values. A position of a usedneighboring sample pair is fixed. In an example, a position of a chromasample in a sample pair is shown in FIGS. 18 and 19 for current CUs 1810and 1910, respectively. In another example, a position of a chromasample in a sample pair is shown in FIG. 20 for a current CU 2010.

In another example, a position of a chroma sample in a sample pair isshown in FIG. 21 for a current CU 2110, wherein sample B can be at anyposition on a longer side of the current CU 2110. As shown in FIG. 21,sample B is at a middle of the longer side of the current CU 2110. Anx-coordinate of sample B may be (width>>1)−1 or (width>>1) or(width>>1)+1.

In another example, a position of a chroma sample in a sample pair isshown in FIG. 22 for a current CU 2210, wherein sample B can be at anyposition on a longer side of the current CU 2210. As shown in FIG. 22,sample B is at a middle of the longer side of the current CU 2210. Anx-coordinate of sample B may be (width>>1)−1 or (width>>1) or(width>>1)+1.

In another example, for 2×N or N×2 chroma blocks, N can be any positiveinteger, such as 2, 4, 8, 16, 32, or 64. When N is equal to two, onlyone neighboring sample pair from left and one neighboring sample pairfrom above are used to calculate maximum and minimum sample values.Otherwise, when N is larger than 2, only one neighboring sample pairfrom a short side and two or more neighboring sample pairs from a longerside of a current CU are used to calculate maximum and minimum samplevalues. A position of a used neighboring sample pair is fixed. In anexample, positions of a chroma sample in a sample pair are shown in FIG.23 for a current CU 2310, wherein sample B is at a middle of a longerside of the current CU 2310 and sample C is at an end of the longerside.

In another example, for a chroma block, when both a width and a heightof a current block are larger than 2, two or more sample pairs from aleft side and two or more sample pairs from an above side of the currentblock are used to calculate maximum and minimum sample values. Aposition of the used neighboring sample pair is fixed. In an example,referring to FIG. 24, for each side of a current CU 2410, one sample isin a middle of a side (such as samples B and C) and another sample is atan end of the side (such as samples A and D).

In another example, for CCLM mode, only a subset of neighboring samplesin specified neighboring samples are used for calculating maximum andminimum sample values. A number of selected samples is dependent on ablock size. In an example, a number of selected neighboring samples arethe same for a left side and an above side. For a chroma block, usedsamples are selected by a specified scanning order. Referring to FIG.25, for a left side of a chroma block 2510, a scanning order is from topto bottom, and for an above side, a scanning order is from left toright. Selected neighboring samples are highlighted with dark circles.Referring to FIG. 26, for a chroma block 2610, used samples are selectedby a reversed scanning order. That is, for a left side of the chromablock 2610, a scanning order is from bottom to top, and for an aboveside, a scanning order is from right to left. A number of selectedneighboring samples are the same for the left side and the above side,and the selected neighboring samples are highlighted with dark circles.

In further embodiments, for CCLM mode, different down-sampling (orinterpolation) filter types are used for left neighboring luma samplesand above neighboring luma samples.

For example, a number of filter types are the same for left and aboveneighboring samples, but a position of filters used for left and aboveneighboring samples are different. Referring to FIG. 27, positions offilters for luma samples in a left side are shown, where W and Xindicate coefficients of the filters, and block indicate positions ofthe luma samples. Referring to FIG. 28, a position of filters for lumasamples in an above side are shown, where W and X indicate coefficientsof the filters, and block indicate positions of the luma samples.

In further embodiments, up to N neighboring reconstructed sample pairsin a specified region are used to calculate parameter a and b in alinear model prediction mode, and N is a positive integer such as 4, 8,or 16. A value of N may be dependent on a block size of a current block.

For example, LIC and CCLM (or any variant of CCLM, such as L_CCLM,T_CCLM, or LT_CCLM) share the same neighboring sample positions tocalculate linear model parameters a and b.

In another example, samples pairs that are used for calculatingparameters a and b of LIC may be located at different positions fordifferent color components. The sample pairs for the different colorcomponents may be decided independently, but an algorithm may be thesame, e.g., using minimum and maximum sample values for deriving modelparameters.

In another example, a first sample pair that is used for calculatingparameters a and b of LIC for one color component (e.g., luma) is firstlocated, then sample pairs that are used for calculating the parametersa and b of LIC for another component (e.g., U or V) is then decided asco-located samples with the first sample pair.

In another example, LIC and CCLM (or any variant of CCLM, such asL_CCLM, T_CCLM, or LT_CCLM) share the same method, such as a leastsquare error method or a max-min method, to calculate linear modelparameters a and b. Both the LIC and CCLM may use the max-min method tocalculate the linear model parameters a and b. For LIC, a sum ofabsolute values of two samples in each sample pair may be used tocalculate maximum and minimum values of a linear model.

In another example, there are three kinds of LIC modes, LT_LIC, L_LIC,and T_LIC. For LT_LIC, both left and above neighboring samples are usedto derive linear model parameters a and b. For L_LIC, only leftneighboring samples are used to derive the linear model parameters a andb. For T_LIC, only top neighboring samples are used to derive the linearmodel parameters a and b. If a current mode is LIC mode, another flagmay be signaled to indicate whether LT_LIC mode is selected. If not, oneadditional flag may be signaled to indicate whether L_LIC mode or T_LICmode is signaled.

In another example, for N×2 or 2×N block, only two neighboring samplepairs are used to calculate parameters a and b in a linear modelprediction mode (or to calculate maximum and minimum sample values).Positions of used neighboring sample pairs are predefined and fixed.

In embodiments of this example, for a linear model prediction mode,which uses both sides for calculating parameters a and b, if both leftand above neighboring samples are available, only one neighboring samplepair from the left and one neighboring sample pair from above are usedto calculate the parameters a and b in the linear model prediction mode.

In embodiments of this example, for a linear model prediction mode,which uses both sides for calculating parameters a and b, if left orabove neighboring samples are not available, two neighboring samplepairs from an available side are used to calculate the parameters a andb in the linear model prediction mode. Two examples of predefinedpositions for current CUs 2910 and 3010 respectively are shown in FIGS.29 and 30.

In embodiments of this example, for a linear model prediction mode,which uses both sides for calculating parameters a and b, if left orabove neighboring samples are not available, the parameters a and b areset to default values, such as a being set to 1 and b being set to 512.

In embodiments of this example, positions of a sample pair are shown inFIGS. 18 and 19.

In embodiments of this example, positions of a sample pair are shown inFIG. 20.

In embodiments of this example, positions of a sample pair are shown inFIG. 21, wherein sample B can be at any position on a longer side of thecurrent CU 2110. For example, sample B may be at a middle of the longerside. In another example, an x-coordinate of sample B may be(width>>1)−1 or (width>>1) or (width>>1)+1.

In embodiments of this example, positions of a sample pair are shown inFIG. 22, wherein sample B can be at any position on a longer side of thecurrent CU 2210. For example, sample B may be on a middle of the longerside. In another example, an x-coordinate of sample B may be(width>>1)−1 or (width>>1) or (width>>1)+1.

In another example, for 2×N or N×2 blocks, N can be any positiveinteger, such as 2, 4, 8, 16, 32, or 64. When N is equal to two, onlyone neighboring sample pair from a left side and one neighboring samplepair from an above side are used to calculate linear model parameters aand b. Otherwise, when N is larger than 2, only one neighboring samplepair from a shorter side and two or more neighboring sample pairs from alonger side are used to calculate the linear model parameters a and b.Positions of a used neighboring sample pair are predefined and fixed.Referring to FIG. 23, positions of a sample pair are shown, whereinSample B can be at any position on a longer side of the current CU 2310.Sample B is in a middle of the longer side, and sample C is at an end ofthe longer side.

In another example, K1 or more sample pairs from a left side and K2 ormore sample pairs from an above side are used to calculate linear modelparameters a and b. Positions of used neighboring sample pairs arepredefined and fixed. In addition, a number of selected samples, such asK, is dependent on a block size. Each of K1 and K2 is a positiveinteger, such as 1, 2, 4, 8. K1 and K2 may be equal.

In embodiments of this example, selected sample pairs on each side areevenly distributed.

In embodiments of this example, values of K1 and K2 should be equal toor smaller than a width and/or a height of a current block.

In embodiments of this example, neighboring samples are selected by aspecified scanning order. A selection process finishes if a number ofthe selected samples meets a specified number. The number of selectedsamples on a left side and an above side may be the same. For the leftside, a scanning order may be from top to bottom, and for the aboveside, a scanning order may be from left to right. Referring to FIGS. 25and 31, selected neighboring samples are highlighted with dark circles.In FIG. 25, the neighboring samples are not down-sampled, and in FIG.31, the neighboring samples are down-sampled. Alternatively, neighboringsamples may be selected by a reversed scanning order. That is, for theleft side, the scanning order may be from bottom to top, and for theabove side, the scanning order may be from right to left. Referring toFIGS. 26, 32 and 33, positions of selected samples are shown. In FIG.26, neighboring samples are not down-sampled, and in FIGS. 32 and 33,neighboring samples are down-sampled.

In embodiments of this example, when both a width and a height of acurrent block are larger than Th, K or more sample pairs from a leftside and two or more sample pairs from an above side are used tocalculate linear model parameters a and b. Positions of used neighboringsample pairs are predefined and fixed. Th is a positive integer, such as2, 4, or 8. K is a positive integer, such as 2, 4, 8.

In embodiments of this example, when left or above neighboring samplesare not available, K samples from an available side are used to deriveparameters a and b.

In another example, for a linear model only using left side or aboveside neighboring samples, such as T_CCLM or L_CCLM, when a size of aused side, such as an above side for T_CCLM or a left side for L_CCLM,is equal to 2, only M samples are used. M is a positive integer, such as2 or 4, and positions are predefined. Otherwise, up to N samples areused. N is a positive integer, such as 4 or 8. M may be not equal to N.

In embodiments of this example, referring to FIG. 34, when a size of aused side is larger than 2, predefined positions are shown, wherein anabove side is used.

In embodiments of this example, referring to FIGS. 35 and 36, when asize of a used side is equal to 2, predefined positions are shown,wherein an above side is used in FIG. 35 and a left side is used in FIG.36.

Further, the proposed methods may be implemented by processing circuitry(e.g., one or more processors or one or more integrated circuits). Inone example, the one or more processors execute a program that is storedin a non-transitory computer-readable medium to perform one or more ofthe proposed methods.

The techniques described above, can be implemented as computer softwareusing computer-readable instructions and physically stored in one ormore computer-readable media. For example, FIG. 37 shows a computersystem (3700) suitable for implementing certain embodiments of thedisclosed subject matter.

The computer software can be coded using any suitable machine code orcomputer language, that may be subject to assembly, compilation,linking, or like mechanisms to create code comprising instructions thatcan be executed directly, or through interpretation, micro-codeexecution, and the like, by computer central processing units (CPUs),Graphics Processing Units (GPUs), and the like.

The instructions can be executed on various types of computers orcomponents thereof, including, for example, personal computers, tabletcomputers, servers, smartphones, gaming devices, internet of thingsdevices, and the like.

The components shown in FIG. 37 for computer system (3700) are exemplaryin nature and are not intended to suggest any limitation as to the scopeof use or functionality of the computer software implementingembodiments of the present disclosure. Neither should the configurationof components be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated in theexemplary embodiment of a computer system (3700).

Computer system (3700) may include certain human interface inputdevices. Such a human interface input device may be responsive to inputby one or more human users through, for example, tactile input (such as:keystrokes, swipes, data glove movements), audio input (such as: voice,clapping), visual input (such as gestures), olfactory input (notdepicted). The human interface devices can also be used to capturecertain media not necessarily directly related to conscious input by ahuman, such as audio (such as: speech, music, ambient sound), images(such as: scanned images, photographic images obtain from a still imagecamera), video (such as two-dimensional video, three-dimensional videoincluding stereoscopic video).

Input human interface devices may include one or more of (only one ofeach depicted): keyboard (3701), mouse (3702), trackpad (3703), touchscreen (3710), data-glove (3704), joystick (3705), microphone (3706),scanner (3707), camera (3708).

Computer system (3700) may also include certain human interface outputdevices. Such human interface output devices may be stimulating thesenses of one or more human users through, for example, tactile output,sound, light, and smell/taste. Such human interface output devices mayinclude tactile output devices (for example tactile feedback by thetouch-screen (3710), data-glove (3704), or joystick (3705), but therecan also be tactile feedback devices that do not serve as inputdevices), audio output devices (such as: speakers 3709, headphones (notdepicted)), visual output devices (such as screens 3710 to includecathode ray tube (CRT) screens, liquid-crystal display (LCD) screens,plasma screens, organic light-emitting diode (OLED) screens, each withor without touch-screen input capability, each with or without tactilefeedback capability—some of which may be capable to output twodimensional visual output or more than three dimensional output throughmeans such as stereographic output; virtual-reality glasses (notdepicted), holographic displays and smoke tanks (not depicted)), andprinters (not depicted).

Computer system (3700) can also include human accessible storage devicesand their associated media such as optical media including CD/DVD ROM/RW(3720) with CD/DVD or the like media (3721), thumb-drive (3722),removable hard drive or solid state drive (3723), legacy magnetic mediasuch as tape and floppy disc (not depicted), specialized ROM/ASIC/PLDbased devices such as security dongles (not depicted), and the like.

Those skilled in the art should also understand that term “computerreadable media” as used in connection with the presently disclosedsubject matter does not encompass transmission media, carrier waves, orother transitory signals.

Computer system (3700) can also include interface(s) to one or morecommunication networks. Networks can for example be wireless, wireline,optical. Networks can further be local, wide-area, metropolitan,vehicular and industrial, real-time, delay-tolerant, and so on. Examplesof networks include local area networks such as Ethernet, wireless LANs,cellular networks to include global systems for mobile communications(GSM), third generation (3G), fourth generation (4G), fifth generation(5G), Long-Term Evolution (LTE), and the like, TV wireline or wirelesswide area digital networks to include cable TV, satellite TV, andterrestrial broadcast TV, vehicular and industrial to include CANBus,and so forth. Certain networks commonly require external networkinterface adapters (3754) that attached to certain general purpose dataports or peripheral buses (3749) (such as, for example universal serialbus (USB) ports of the computer system (3700) to communicate with anexternal network (3755); others are commonly integrated into the core ofthe computer system (3700) by attachment to a system bus as describedbelow (for example Ethernet interface into a PC computer system orcellular network interface into a smartphone computer system). Using anyof these networks, computer system (3700) can communicate with otherentities. Such communication can be uni-directional, receive only (forexample, broadcast TV), uni-directional send-only (for example CANbus tocertain CANbus devices), or bi-directional, for example to othercomputer systems using local or wide area digital networks. Certainprotocols and protocol stacks can be used on each of those networks andnetwork interfaces as described above.

Aforementioned human interface devices, human-accessible storagedevices, and network interfaces can be attached to a core (3740) of thecomputer system (3700).

The core (3740) can include one or more Central Processing Units (CPU)(3741), Graphics Processing Units (GPU) 3742, specialized programmableprocessing units in the form of Field Programmable Gate Areas (FPGA)(3743), hardware accelerators for certain tasks (3744), and so forth.These devices, along with Read-only memory (ROM) (3745), Random-accessmemory (RAM) (3746), internal mass storage such as internal non-useraccessible hard drives, solid-state drives (SSDs), and the like (3747),may be connected through a system bus (3748). In some computer systems,the system bus (3748) can be accessible in the form of one or morephysical plugs to enable extensions by additional CPUs, GPU, and thelike. The peripheral devices can be attached either directly to thecore's system bus (3748), or through a peripheral bus 3749.Architectures for a peripheral bus include peripheral componentinterconnect (PCI), USB, and the like.

CPUs (3741), GPUs (3742), FPGAs (3743), and accelerators (3744) canexecute certain instructions that, in combination, can make up theaforementioned computer code. That computer code can be stored in ROM(3745) or RAM (3746). Transitional data can be also be stored in RAM(3746), whereas permanent data can be stored for example, in theinternal mass storage (3747). Fast storage and retrieve to any of thememory devices can be enabled through the use of cache memory, that canbe closely associated with one or more CPU (3741), GPU (3742), massstorage (3747), ROM (3745), RAM (3746), and the like.

The computer readable media can have computer code thereon forperforming various computer-implemented operations. The media andcomputer code can be those specially designed and constructed for thepurposes of the present disclosure, or they can be of the kind wellknown and available to those having skill in the computer software arts.

As an example and not by way of limitation, the computer system havingarchitecture (3700), and specifically the core (3740) can providefunctionality as a result of processor(s) (including CPUs, GPUs, FPGA,accelerators, and the like) executing software embodied in one or moretangible, computer-readable media. Such computer-readable media can bemedia associated with user-accessible mass storage as introduced above,as well as certain storage of the core (3740) that are of non-transitorynature, such as core-internal mass storage (3747) or ROM (3745). Thesoftware implementing various embodiments of the present disclosure canbe stored in such devices and executed by core (3740). Acomputer-readable medium can include one or more memory devices orchips, according to particular needs. The software can cause the core(3740) and specifically the processors therein (including CPU, GPU,FPGA, and the like) to execute particular processes or particular partsof particular processes described herein, including defining datastructures stored in RAM (3746) and modifying such data structuresaccording to the processes defined by the software. In addition or as analternative, the computer system can provide functionality as a resultof logic hardwired or otherwise embodied in a circuit (for example:accelerator 3744), which can operate in place of or together withsoftware to execute particular processes or particular parts ofparticular processes described herein. Reference to software canencompass logic, and vice versa, where appropriate. Reference to acomputer-readable media can encompass a circuit (such as an integratedcircuit (IC)) storing software for execution, a circuit embodying logicfor execution, or both, where appropriate. The present disclosureencompasses any suitable combination of hardware and software.

While this disclosure has described several exemplary embodiments, thereare alterations, permutations, and various substitute equivalents, whichfall within the scope of the disclosure. It will thus be appreciatedthat those skilled in the art will be able to devise numerous systemsand methods which, although not explicitly shown or described herein,embody the principles of the disclosure and are thus within the spiritand scope thereof.

1. A device for encoding or decoding a video sequence, the devicecomprising: at least one memory configured to store program code; atleast one processor configured to read the program code and operate asinstructed by the program code, the program code including: firstencoding or decoding code configured to cause the at least one processorto apply a Cross-Component Linear Model (CCLM) to a video sequence andapply an interpolation filter in the Cross-Component Linear Model(CCLM), first obtaining code configured to cause the at least oneprocessor to obtain an absolute difference between maximum and minimumvalues of a plurality of neighboring samples of a first one among a lumablock and related to the video sequence; dividing code configured tocause the at least one processor to divide the absolute difference intointervals; first performing code configured to cause the at least oneprocessor to perform a non-uniform quantization of the absolutedifference; second obtaining code configured to cause the at least oneprocessor to obtain a floor value based on by using, in a look up table,the absolute difference of which the non-uniform quantization isperformed; third obtaining code configured to cause the at least oneprocessor to obtain a derived value by using at least the absolutedifference and the intervals based on a look-up table; and predictingcode configured to cause the at least one processor to predict a sampleof a different one among a chroma block related to the video sequence,based on the derived value, wherein the interpolation filter isdependent upon a YUV format of the video sequence.
 2. The device ofclaim 1, wherein the first encoding or decoding code further comprisescode configured to cause the at least one processor to use taps of theinterpolation filter applied, in the Cross-Component Linear Model(CCLM), that are dependent upon the YUV format of the video sequence. 3.The device of claim 1, wherein the first encoding or decoding codefurther comprises code configured to cause the at least one processor toset the format of the interpolation filter, applied in theCross-Component Linear Model (CCLM), to be the same for the format ofthe video sequence when the video sequence includes a YUV format of4:4:4: or 4:2:2, and set the format of the interpolation filter to bedifferent than the format of the video sequence when the video sequenceincludes a YUV format of 4:2:0.
 4. The device of claim 2, wherein thefirst encoding or decoding code is configured such that the taps of theinterpolation filter used in the Cross-Component Linear Model (CCLM) arein the same form as the YUV format of the video sequence.
 5. The deviceof claim 1, wherein the first encoding or decoding code furthercomprises code configured to cause the at least one processor to usetaps of the interpolation filter applied, in the Cross-Component LinearModel (CCLM), that are different for different YUV formats of the videosequence.
 6. The device of claim 1, wherein the first encoding ordecoding code is configured to apply the Cross-Component Linear Model(CCLM) and apply the interpolation filter in the Cross-Component LinearModel (CCLM) by setting the interpolation filter to be different for topand left neighboring luma reconstructed samples.
 7. The device of claim6, wherein the first encoding or decoding code is configured to applythe Cross-Component Linear Model (CCLM) and apply the interpolationfilter in the Cross-Component Linear Model (CCLM) to the top and leftneighboring luma reconstructed samples, such that the interpolationfilter is dependent on the YUV format of the video sequence.
 8. Thedevice of claim 1, wherein the first encoding or decoding code isfurther configured to cause the at least one processor to set a numberof lines in top neighboring luma samples and a number of columns in leftneighboring luma samples used in the Cross-Component Linear Model (CCLM)to be dependent on the YUV format of the video sequence.
 9. The deviceof claim 8, wherein the first encoding or decoding code is furtherconfigured to cause the at least one processor to use at least one ofone row in a top neighboring region and one column in a left neighboringregion in the Cross-Component Linear Model (CCLM) for video sequenceshaving one of a YUV format of 4:4:4 or 4:2:2.
 10. The device of claim 8,wherein the first encoding or decoding code is further configured tocause the at least one processor to use at least one of one row in a topneighboring region and at least two columns in a left neighboring regionin the Cross-Component Linear Model (CCLM) for video sequences having aYUV format of 4:2:2.
 11. The device of claim 1, wherein the firstobtaining code is further configured to cause the at least one processorto obtain the maximum and minimum values, using N neighboring samplepairs of the luma block and the chroma block, N being a positive integerof one among 4, 8 and 16, wherein each of the N neighboring sample pairscomprises a first neighboring sample at a first location neighboring thefirst one among the luma block and the chroma block, and a secondneighboring sample at a second location neighboring the second one amongthe luma block and the chroma block and corresponding to the firstlocation neighboring the first one among the luma block and the chromablock.
 12. The device of claim 11, wherein the first obtaining code isfurther configured to cause the at least one processor to select thefirst neighboring sample by scanning the plurality of neighboringsamples from bottom to top and/or from right to left.
 13. The device ofclaim 1, wherein the program code further comprises: fourth obtainingcode configured to cause the at least one processor to obtain a scalingfactor and an offset of a linear model for local illuminationcompensation (LIC) of a current block, using N neighboring sample pairsof the current block and a reference block, N being a positive integerof one among 4, 8 and 16, wherein each of the N neighboring sample pairscomprises a first neighboring sample at a first location neighboring thecurrent block, and a second neighboring sample at a second locationneighboring the reference block and corresponding to the first locationneighboring the current block; and second performing code configured tocause the at least one processor to perform the local illuminationcompensation (LIC) of the current block, using the obtained scalingfactor and the obtained offset.
 14. The device of claim 13, wherein N isdependent on a block size of the current block.
 15. A non-transitorycomputer-readable medium storing program code, the program codecomprising one or more instructions that, when executed by one or moreprocessors of a device, cause the one or more processors to: apply aCross-Component Linear Model (CCLM) to a video sequence and apply aninterpolation filter in the Cross-Component Linear Model (CCLM), obtainan absolute difference between maximum and minimum values of a pluralityof neighboring samples of a first one among a luma block and related tothe video sequence; divide the obtained absolute difference intointervals; perform a non-uniform quantization of the obtained absolutedifference; obtain a floor value based on the absolute difference ofwhich the non-uniform quantization is performed; obtain a derived valueby using at least the absolute difference and the intervals based on alook-up table; and predict a sample of a different one among a chromablock related to the video sequence, based on the derived value, whereinthe interpolation filter is dependent upon a YUV format of the videosequence.
 16. The non-transitory computer-readable medium storingprogram code of claim 15, wherein in the applying of the interpolationfilter in the Cross-Component Linear Model (CCLM), the program codefurther comprises one or more instructions that, when executed by theone or more processors, causes the one or more processors to use taps ofthe interpolation filter that are dependent upon the YUV format of thevideo sequence.
 17. The non-transitory computer-readable medium storingprogram code of claim 15, wherein in the application of theCross-Component Linear Model (CCLM) taps of the interpolation filter areused in the Cross-Component Linear Model (CCLM) that are in the sameform as the YUV format of the video sequence.
 18. The non-transitorycomputer-readable medium storing program code of claim 15, wherein inthe application of the Cross-Component Linear Model (CCLM) the format ofthe interpolation filter is set to be the same for the format of thevideo sequence when the video sequence includes a YUV format of 4:4:4:or 4:2:2, and the format of the interpolation filter is set to bedifferent than the format of the video sequence when the video sequenceincludes a YUV format of 4:2:0.
 19. The non-transitory computer-readablemedium storing program code of claim 15, wherein in the application ofthe Cross-Component Linear Model (CCLM) taps of the interpolation filterare used, which are different for different YUV formats of the videosequence.
 20. The non-transitory computer-readable medium storingprogram code of claim 15, wherein in the application of theCross-Component Linear Model (CCLM) the interpolation filter is set tobe different for top and left neighboring luma reconstructed samples.